def normalized_hist_dataframe(data_column,
bin_number=50,
output_dir='/var/tmp/'):
"""
Create a histogram using 2019AStrpi database with just the column name as an input
Args:
data_column (str): Name of the column to index
bin_number (int): Number of bins for the histogram
output_dir (str): Directory to save figure to
Returns:
fig: The figure object the hisotgram was plotted on
ax: The axis of the figure the histogram was plotted on
"""
db = celldatabase.load_hdf(
"/var/tmp/figuresdata/2019astrpi/direct_and_indirect_cells.h5")
# dbTuned = db.query(studyparams.TUNING_FILTER)
D1DB = db.query(studyparams.D1_CELLS)
nD1DB = db.query(studyparams.nD1_CELLS)
D1DB = D1DB.replace([np.inf, -np.inf], np.nan)
nD1DB = nD1DB.replace([np.inf, -np.inf], np.nan)
D1DB = D1DB[D1DB[data_column].notnull()]
nD1DB = nD1DB[nD1DB[data_column].notnull()]
D1Hist, D1bins = np.histogram(D1DB[data_column],
bins=bin_number,
density=True)
nD1Hist, nD1bins = np.histogram(nD1DB[data_column],
bins=bin_number,
density=True)
center = (D1bins[:-1] + D1bins[1:]) / 2
width = 0.7 * (D1bins[1] - D1bins[0])
D1Median = np.median(D1DB[data_column])
nD1Median = np.median(nD1DB[data_column])
fig = plt.gcf()
fig.clf()
figFilename = "{}".format(data_column) # Do not include extension
figFormat = 'png' # 'pdf' or 'svg'
figSize = [5, 5]
ax = fig.add_subplot()
ax.bar(center, D1Hist, width=width, align='center', label='D1', alpha=0.5)
ax.bar(center,
nD1Hist,
width=width,
align='center',
label='nD1',
alpha=0.5)
ax.legend()
ax.set_xlabel('{} value'.format(data_column))
ax.set_ylabel('Frequency')
ax.set_title(data_column)
ymin, ymax = ax.get_ybound()
ax.vlines(D1Median, ymin, ymax, color="Green")
ax.vlines(nD1Median, ymin, ymax, color="Red")
extraplots.save_figure(figFilename, figFormat, figSize, output_dir, 'w')
plt.show()
return fig, ax
cellsThisArea = brainAreaEachCell == brainArea
absSpikeDifEachCellThisArea = absSpikeDifEachCell[:, cellsThisArea]
maxDifBinEachCellThisArea = np.argmax(absSpikeDifEachCellThisArea, axis=0)
cellReInd = np.argsort(maxDifBinEachCellThisArea)
sortedAbsSpikeDifEachCellThisArea = absSpikeDifEachCellThisArea[:,
cellReInd]
sortedAbsSpikeDifEachCell[:,
cellsThisArea] = sortedAbsSpikeDifEachCellThisArea
ax = plt.subplot(1, 2, indA + 1)
ax.imshow(np.transpose(sortedAbsSpikeDifEachCellThisArea),
origin='lower',
cmap='viridis',
interpolation='nearest')
ax.set_xticks(range(numOfBins +
1)[::10]) #np.arange(len(timeBinEdges))[::10])
ax.set_xticklabels(
np.arange(timePeriodToPlot[0], timePeriodToPlot[1] + 0.01,
binWidth * 10))
#xticklabels = ['{:.1f}'.format(x) for x in xticks]
#ax.xaxis.set_major_formatter(ticker.StrMethodFormatter("{x:g}"))
#plt.yticks([150, 50], brainAreaLabels)
ax.set_yticks([0, 50, 100])
ax.set_ylabel('{}\nCell number'.format(brainArea))
ax.set_xlabel('Time from movement onset (sec)')
if SAVE_FIGURE:
extraplots.save_figure(figFilename, figFormat, figSize, outputDir)
plt.show()
ftraceEachTrial[cellInd1, trialsHighFreq]
]).T
dataPoints = np.vstack([class0, class1])
dataLabels = np.concatenate([np.zeros(nClass0), np.ones(nClass1)])
clf = svm.SVC(kernel='linear', C=10)
clf.fit(dataPoints, dataLabels)
# -- Plot boundary --
#plot_contours(ax0, clf, dataPoints, dataLabels)
xlim = ax0.get_xlim()
ylim = ax0.get_ylim()
xx = np.linspace(xlim[0], xlim[1], 4)
yy = np.linspace(ylim[0], ylim[1], 4)
YY, XX = np.meshgrid(yy, xx)
xy = np.vstack([XX.ravel(), YY.ravel()]).T
Z = clf.decision_function(xy).reshape(XX.shape)
colorBoundary = cp.TangoPalette['Chameleon2']
#ax0.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,
# linestyles=['--', '-', '--'])
hbound = ax0.contour(XX,
YY,
Z,
colors=colorBoundary,
levels=[0],
linestyles=['-'],
zorder=-1)
SAVEFIG = 0
if SAVEFIG:
figname = 'twocell_scatter'
extraplots.save_figure(figname, 'svg', [3.5, 3.5], outputDir='/tmp/')
plt.plot(xvals, schedData[featureToPlot], 'o', mfc='none', mec=pointsColor)
thisMedian = dataMedian[featureToPlot][schedName]
plt.bar(schedMapping[schedName],
thisMedian,
width=0.75,
lw=2,
fc=barFaceColor,
ec=barEdgeColor)
#plt.ylabel(featureToPlot, fontsize=labelFontSize)
plt.ylabel('Performance (%)', fontsize=labelsFontSize)
ax0.set_xticks(np.arange(nSched))
ax0.set_xticklabels(schedSorted, fontsize=labelsFontSize)
if featureToPlot == 'Difference':
plt.ylim([-20, 50])
else:
plt.ylim([50, 100])
extraplots.boxoff(ax0)
extraplots.set_ticks_fontsize(ax0, ticksFontSize)
plt.show()
SAVEFIG = 1
if SAVEFIG:
figname = 'behavior_effect_{}'.format(featureToPlot)
extraplots.save_figure(figname,
'svg', [4.5, 3],
facecolor='w',
outputDir='/tmp/')
xTickPeriod=1,
xscale='linear')
(plineL, pcapsL, pbarsL,
pdotsL) = extraplots.plot_psychometric(1e-3 * possibleValuesL,
fractionHitsEachValueL,
ciHitsEachValueL,
xTickPeriod=1,
xscale='linear')
plt.setp([pline, plineL], lw=3)
plt.setp([plineL, pcapsL, pbarsL, pdotsL], color=laserColor)
plt.setp(pdotsL, mfc=laserColor)
#plt.setp([plineL,pcapsL,pbarsL,pdotsL],visible=False)
#plt.setp([pline,pcaps,pbars,pdots],visible=False)
plt.xlabel('Stimulus', fontsize=fontsize)
plt.ylabel('Rightward trials (%)', fontsize=fontsize)
extraplots.set_ticks_fontsize(plt.gca(), fontsize)
plt.legend([pline, plineL], ['Control', 'Laser'], loc='lower right')
plt.title(subject)
plt.show()
sys.exit()
'''
extraplots.save_figure('/tmp/bili002_laser','pdf',[5,5],outputDir='/tmp/')
extraplots.save_figure('/tmp/bili002_control','pdf',[5,5],outputDir='/tmp/')
extraplots.save_figure('/tmp/bili006_laser','pdf',[5,5],outputDir='/tmp/')
extraplots.save_figure('/tmp/bili002_control','pdf',[5,5],outputDir='/tmp/')
'''
extraplots.boxoff(plt.gca())
plt.xlabel('Time (s)', fontsize=fontSizeLabels)
plt.ylabel('Amplitude (V)', fontsize=fontSizeLabels)
extraplots.set_ticks_fontsize(plt.gca(),fontSizeTicks)
ax1.annotate('A', xy=(labelPosX[0],labelPosY[0]), xycoords='figure fraction',
fontsize=fontSizePanel, fontweight='bold')
ax2 = plt.subplot(gs[1,:-1])
plt.plot(np.random.randn(20),np.random.randn(20),'o-',ms=markerSize,color='0.75',mfc=distanceColor,mec='w')
extraplots.boxoff(plt.gca())
extraplots.set_ticks_fontsize(plt.gca(),fontSizeTicks)
plt.xlabel('Time (s)', fontsize=fontSizeLabels)
plt.ylabel('Distance (m)', fontsize=fontSizeLabels)
ax2.annotate('B', xy=(labelPosX[0],labelPosY[1]), xycoords='figure fraction', fontsize=fontSizePanel, fontweight='bold')
ax3 = plt.subplot(gs[1:, -1])
plt.plot(np.random.randn(20),np.random.randn(20),'o-',ms=markerSize,color='0.75',mfc=velocityColor,mec='w')
extraplots.set_ticks_fontsize(plt.gca(),fontSizeTicks)
plt.xlabel('Time (s)', fontsize=fontSizeLabels)
plt.ylabel('Velocity (m/s)', fontsize=fontSizeLabels)
xLims = [-2,2]
ax3.set_xlim(xLims)
ax3.set_xticks(np.arange(xLims[0],xLims[1]+1))
ax3.annotate('C', xy=(labelPosX[1],labelPosY[1]), xycoords='figure fraction', fontsize=fontSizePanel, fontweight='bold')
plt.show()
if SAVE_FIGURE:
extraplots.save_figure(figFilename, figFormat, figSize, outputDir)
ax2 = plt.subplot(gs[1, :-1])
plt.plot(np.random.randn(20), np.random.randn(20), 'o', mfc='none')
extraplots.boxoff(plt.gca())
extraplots.set_ticks_fontsize(plt.gca(), fontSizeTicks)
plt.xlabel('Time (s)', fontsize=fontSizeLabels)
plt.ylabel('Amplitude (V)', fontsize=fontSizeLabels)
ax2.annotate('B',
xy=(labelPosX[0], labelPosY[1]),
xycoords='figure fraction',
fontsize=fontSizePanel,
fontweight='bold')
ax3 = plt.subplot(gs[1:, -1])
plt.plot(np.random.randn(20), np.random.randn(20), 'o', mfc='none')
extraplots.set_ticks_fontsize(plt.gca(), fontSizeTicks)
plt.xlabel('Time (s)', fontsize=fontSizeLabels)
plt.ylabel('Amplitude (V)', fontsize=fontSizeLabels)
ax2.annotate('C',
xy=(labelPosX[1], labelPosY[1]),
xycoords='figure fraction',
fontsize=fontSizePanel,
fontweight='bold')
plt.show()
figFormat = 'pdf' #'svg'
figFilename = 'testfig' # Do not include extension
if PRINT_FIGURE:
extraplots.save_figure(figFilename, figFormat, [8, 6], outputDir)
for band in range(len(numBands)):
bias = np.zeros(len(numLasers))
for laser in range(len(numLasers)):
trialsThisCond = trialsEachCond[:, laser, band]
bias[laser] = 1.0 * (np.sum(toneChoice[trialsThisCond]) - np.sum(noiseChoice[trialsThisCond])) / \
(np.sum(toneChoice[trialsThisCond]) + np.sum(noiseChoice[trialsThisCond]))
xVals = barLoc + xLocs[band]
plt.plot(xVals, bias, 'k-', lw=2)
l1, = plt.plot(xVals[0], bias[0], 'o', mec='k', mfc='k', ms=10)
l2, = plt.plot(xVals[1], bias[1], 'o', mec='k', mfc='white', ms=10)
axBias.legend([l1, l2], ['control', legendLabel[indType]])
axBias.set_xlim(xLocs[0] - 0.5, xLocs[-1] + 0.5)
axBias.set_xticks(xLocs)
axBias.set_xticklabels(numBands)
axBias.set_xlabel('Masker bandwidth (oct)')
yLims = axBias.get_ylim()
axBias.set_ylim(yLims[0] - 0.1, yLims[1] + 0.1)
axBias.set_ylabel('Bias')
extraplots.boxoff(axBias)
plt.suptitle(mouse + ' ({})'.format(mouseLabel[indType]))
figFilename = '{}_behav_report'.format(mouse)
extraplots.save_figure(figFilename, 'png', [9, 4], '/tmp/')
# tractFig = fig1
# sliceNum = 207
if tract['atlasZ']<zBound:
tractAx = axs[0]
tractFig = figs[0]
sliceNum = 202
elif (tract['atlasZ']>zBound):
tractAx = axs[1]
tractFig = figs[1]
sliceNum = 209
plt.sca(tractAx)
plt.figure(tractFig.number)
tractAx.imshow(np.rot90(atlas[:,:,sliceNum], -1), 'gray')
plt.hold(1)
# tractAx.plot([tract['tipCoords'][0], tract['brainSurfCoords'][0]], [tract['tipCoords'][1], tract['brainSurfCoords'][1]],
# color=lineColor, linewidth=lineWidth)
print tract['brainSurfCoords']
for coordPair in tract['siteCoords']:
tractAx.plot(coordPair[0], coordPair[1], '.', mfc=markerColor, mec=markerColor, ms = 2)
plt.hold(1)
for indFig, fig in enumerate(figs):
plt.figure(fig.number)
extraplots.save_figure('AC_tracts{}'.format(indFig+1), 'svg', (5, 3), outputDir=figDir)
# extraplots.save_figure('tractsAC', 'svg', (5, 3), outputDir='/tmp')
plt.show()
tractAx = axs[1]
tractFig = figs[1]
sliceNum = 193
elif (tract['atlasZ'] > zBounds[1]):
tractAx = axs[2]
tractFig = figs[2]
sliceNum = 206
plt.sca(tractAx)
plt.figure(tractFig.number)
tractAx.imshow(np.rot90(atlas[:, :, sliceNum], -1), 'gray')
plt.hold(1)
tractAx.plot([tract['tipCoords'][0], tract['brainSurfCoords'][0]],
[tract['tipCoords'][1], tract['brainSurfCoords'][1]],
color=lineColor,
linewidth=lineWidth)
for coordPair in tract['siteCoords']:
tractAx.plot(coordPair[0],
coordPair[1],
'*',
mfc=markerColor,
mec=markerColor)
plt.hold(1)
for indFig, fig in enumerate(figs):
plt.figure(fig.number)
extraplots.save_figure('tracts{}'.format(indFig + 1),
'svg', (5, 3),
outputDir='/tmp')
plt.show()
ax2 = plt.subplot(gs[1,:-2])
trialsThisSecondVal = trialsEachCond[:, :, 1]
pRaster, hcond, zline = extraplots.raster_plot(spikeTimesFromEventOnset,
indexLimitsEachTrial,
timeRange = [-0.2, 1.5],
trialsEachCond=trialsThisSecondVal,
labels=firstSortLabels)
plt.setp(pRaster, ms=4)
extraplots.boxoff(plt.gca())
extraplots.set_ticks_fontsize(plt.gca(),fontSizeTicks)
plt.xlabel('Time from stimulus onset (s)', fontsize=fontSizeLabels)
plt.ylabel('Bandwidth (octaves)', fontsize=fontSizeLabels)
ax2.annotate('B', xy=(labelPosX[0],labelPosY[1]), xycoords='figure fraction', fontsize=fontSizePanel, fontweight='bold')
ax3 = plt.subplot(gs[0:, -2:])
spikeArray, errorArray, baseSpikeRate = bandwidths_analysis.band_select(spikeTimestamps, eventOnsetTimes, ampEachTrial, bandEachTrial, timeRange = [0,1])
bandwidths_analysis.band_select_plot(spikeArray, errorArray, baseSpikeRate, np.unique(bandEachTrial), legend = True, labels = ['54 dB', '66 dB'])
extraplots.set_ticks_fontsize(plt.gca(),fontSizeTicks)
plt.xlabel('Bandwidth (octaves)', fontsize=fontSizeLabels)
plt.ylabel('Average number of spikes during stimulus', fontsize=fontSizeLabels)
ax3.annotate('C', xy=(labelPosX[1],labelPosY[0]), xycoords='figure fraction', fontsize=fontSizePanel, fontweight='bold')
plt.show()
figFormat = 'pdf' #'svg'
figFilename = 'testfig' # Do not include extension
if PRINT_FIGURE:
extraplots.save_figure(figFilename, figFormat, [8,6], outputDir)
'o',
mec='k',
mfc='k',
ms=10)
l2, = plt.plot(xVals[1],
threshold[1],
'o',
mec='k',
mfc='white',
ms=10)
if all(threshold):
axThresh.legend([l1, l2], ['control', legendLabel[indType]])
axThresh.set_xlim(xLocs[0] - 0.5, xLocs[-1] + 0.5)
axThresh.set_xticks(xLocs)
axThresh.set_xticklabels(numBands)
axThresh.set_xlabel('Masker bandwidth (oct)')
yLims = axThresh.get_ylim()
axThresh.set_ylim(yLims[0] - 10, yLims[1] + 10)
axThresh.set_ylabel('Detection threshold (dB)')
extraplots.boxoff(axThresh)
plt.suptitle(mouse + ' ({})'.format(mouseLabel[indType]))
figFilename = '{}_logistic_fit_report'.format(mouse)
extraplots.save_figure(figFilename, 'png', [3 * len(numBands) + 2, 4],
'/tmp/')
exampleCells.append(cell)
if CASE==0:
if len(sys.argv)>1:
cellInd = int(sys.argv[1])
else:
print "Use sys.argv to select a cell. Defaulting to cell 0"
cellInd = 0
cellDict = exampleCells[cellInd]
cell = find_cell(db, cellDict['subject'], cellDict['date'], cellDict['depth'], cellDict['tetrode'], cellDict['cluster']).iloc[0]
am_example(cell)
plt.show()
figFilename = 'exampleAM_{}'.format(cellInd)
print 'Saving {}'.format(figFilename)
extraplots.save_figure(figFilename, 'svg', [6,10], '/tmp/')
if CASE==1:
#Rsync all the example data
for cellDict in exampleCells:
cell = find_cell(db, cellDict['subject'], cellDict['date'], cellDict['depth'], cellDict['tetrode'], cellDict['cluster']).iloc[0]
amInd = celldatabase.get_session_inds(cell, 'am')[0]
amSession = cell['ephys'][amInd]
rsync_session_data(cell['subject'], amSession)
rsync_session_data(cell['subject'], '{}_kk'.format(amSession))
amBehav = cell['behavior'][amInd]
rsync_behavior(cell['subject'], amBehav)
if CASE==2:
### THIS IS FOR PLOTTING ALL CELLS BY THEIR MAX SYNC FREQ
ax = plt.subplot2grid((2, len(allSessions)), (1, indSession))
chordEstimates[
indSession, :] = behavioranalysis.plot_psycurve_fit_and_data(
theseDataObjs[1], thisColor)
ax.tick_params(axis='both', which='major', labelsize=labelFontSize)
ax.tick_params(axis='both', which='minor', labelsize=labelFontSize)
# plt.title('{}'.format([key for key, value in theseSoundTypes.iteritems()][1]))
behavioranalysis.nice_psycurve_settings(ax,
fontsize=10,
lineweight=2,
fitlineinds=[0])
if indSession > 0:
ax.set_ylabel('')
else:
ax.set_ylabel('Chords\nFraction rightward trials')
figSize = [30, 6]
# figtext(0.075, 0.7, 'Fraction of trials going to the right', rotation='vertical')
# figtext(0.4, 0.05, 'Log2(frequency) - octaves')
plt.suptitle(animal)
plt.gcf().set_facecolor('w')
outputDir = '/home/nick/data/dissertation_amod'
extraplots.save_figure('{}_muscimol_inactivation_daily'.format(animal),
'png',
figSize,
outputDir=outputDir)
plt.show()